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LLM Inference Fundamentals for the Routing Engineer1 / 3

1 min lesson

Self-hosted vs provider-hosted

Use "A mature platform does both and routes between them" to say what you would do next.

Step 1 of 3

Self-hosted vs provider-hostedthe tradeoff that frames the whole role

This is the strategic fork the inference platform lives on. Hosting your own models on rented or owned GPUs gives you control and the best cost-at-scale, at the price of running a serving stack, capacity planning and on-call. Calling a provider's API gives you flexibility and operational simplicity, at the price of per-token margin, rate limits and being exposed to their outages. A mature platform does both and routes between them.

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Advanced table

Reference table

Dimension
Cost at scale
Self-hosted
Lowest if utilization is high
Provider-hosted
Per-token margin baked in
Dimension
Control / tuning
Self-hosted
Full (batching, KV paging, quantization)
Provider-hosted
Limited to API knobs
Dimension
Operational burden
Self-hosted
You run serving + on-call + capacity
Provider-hosted
Provider runs it
Dimension
Flexibility / new models
Self-hosted
You must deploy each one
Provider-hosted
New models appear behind one API
Dimension
Failure exposure
Self-hosted
Your fleet, your blast radiusHow much breaks if a change goes wrong; the scope of potential damage. Press Enter for the full definition.
Provider-hosted
Their outage degrades you unless you fail over

The honest answer in an interview is "it depends on volume, latency budget and which models" - then reason it.